The digital revolution of the past few years has led to the use of digital technology in most areas. Automated manufacturing has given rise to faster, more efficient machines and better quality goods. As part of automated manufacturing, robots and machines are now able to perform quality assurance testing. Goods automatically manufactured can be inspected by machines faster than a human can and with better accuracy. However, one issue with this is that such machines need to be properly programmed or “trained” to find defects and issues with the manufactured goods.
Automated quality assurance testing to spot defects in manufactured goods is a combination of using computer vision and pattern recognition as well as artificial intelligence. In one type of quality assurance testing, computer vision systems would use digital cameras to inspect the relevant surfaces of manufactured goods.
Any blemishes and/or surface imperfections would be detected and the QA system would determine if the imperfection is a defect in the manufactured good or not. To determine if a defect has been found, the system would need to be “trained” to recognize defects and this can be done by using AI and pattern recognition to differentiate between known defects, defects previously encountered, and a simple imperfection. (Of course, depending on the industry, any imperfection might be considered as a defect. As an example, in the microprocessor manufacturing industry, any imperfection on the manufactured die would be considered a flaw or a defect.)
To train such systems, especially when AI is being used for pattern recognition, it is usual to provide the system with a large number of examples of previously encountered manufacturing defects. The system then “learns” to recognize images of defects in much the same way that current image recognition systems learn to recognize human faces in digital images. Thus, since defects come in all shapes, sizes, and types, to be able to recognize a specific type of defect, large numbers of images of that type of defect is preferably available. These images of that type of defect are then presented to the system as training data. The system's logic (whether implemented as a convolutional neural network or as some other form of artificial intelligence) then learns to recognize that type of defect in the images.
Current systems are suitable for the above described manufacturing methods and QA processes. However, there are some defects that can be quite rare and, because of their rarity, not a lot of images of these defects are available. Because of the paucity of such images, current systems are either unable to be trained to detect such defects or, more commonly, such systems are improperly trained. Improperly trained systems would therefore not recognize such defects, leading to issues with the finished product.
Based on the above, there is therefore a need for systems and methods which would allow for such current systems to be properly trained in the detection and recognition of such rare defects.